Overview

Dataset statistics

Number of variables20
Number of observations50000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory160.0 B

Variable types

Text1
Numeric13
Categorical6

Alerts

age is highly overall correlated with credit_history_years and 1 other fieldsHigh correlation
annual_income is highly overall correlated with current_debt and 1 other fieldsHigh correlation
credit_history_years is highly overall correlated with age and 1 other fieldsHigh correlation
credit_score is highly overall correlated with loan_statusHigh correlation
current_debt is highly overall correlated with annual_income and 1 other fieldsHigh correlation
debt_to_income_ratio is highly overall correlated with current_debtHigh correlation
interest_rate is highly overall correlated with product_typeHigh correlation
loan_amount is highly overall correlated with annual_income and 2 other fieldsHigh correlation
loan_status is highly overall correlated with credit_scoreHigh correlation
loan_to_income_ratio is highly overall correlated with loan_amount and 1 other fieldsHigh correlation
payment_to_income_ratio is highly overall correlated with loan_amount and 1 other fieldsHigh correlation
product_type is highly overall correlated with interest_rateHigh correlation
savings_assets is highly overall correlated with credit_history_yearsHigh correlation
years_employed is highly overall correlated with ageHigh correlation
defaults_on_file is highly imbalanced (69.9%)Imbalance
derogatory_marks is highly imbalanced (72.6%)Imbalance
customer_id has unique valuesUnique
years_employed has 3459 (6.9%) zerosZeros
delinquencies_last_2yrs has 30797 (61.6%) zerosZeros

Reproduction

Analysis started2025-11-25 13:46:57.420338
Analysis finished2025-11-25 13:47:23.995886
Duration26.58 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

customer_id
Text

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:24.344836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters500000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50000 ?
Unique (%)100.0%

Sample

1st rowCUST100000
2nd rowCUST100001
3rd rowCUST100002
4th rowCUST100003
5th rowCUST100004
ValueCountFrequency (%)
cust1000001
 
< 0.1%
cust1000471
 
< 0.1%
cust1000231
 
< 0.1%
cust1000111
 
< 0.1%
cust1000021
 
< 0.1%
cust1000031
 
< 0.1%
cust1000041
 
< 0.1%
cust1000051
 
< 0.1%
cust1000061
 
< 0.1%
cust1000071
 
< 0.1%
Other values (49990)49990
> 99.9%
2025-11-25T13:47:24.767771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
180000
16.0%
C50000
10.0%
U50000
10.0%
S50000
10.0%
T50000
10.0%
030000
 
6.0%
330000
 
6.0%
230000
 
6.0%
430000
 
6.0%
520000
 
4.0%
Other values (4)80000
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
180000
16.0%
C50000
10.0%
U50000
10.0%
S50000
10.0%
T50000
10.0%
030000
 
6.0%
330000
 
6.0%
230000
 
6.0%
430000
 
6.0%
520000
 
4.0%
Other values (4)80000
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
180000
16.0%
C50000
10.0%
U50000
10.0%
S50000
10.0%
T50000
10.0%
030000
 
6.0%
330000
 
6.0%
230000
 
6.0%
430000
 
6.0%
520000
 
4.0%
Other values (4)80000
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
180000
16.0%
C50000
10.0%
U50000
10.0%
S50000
10.0%
T50000
10.0%
030000
 
6.0%
330000
 
6.0%
230000
 
6.0%
430000
 
6.0%
520000
 
4.0%
Other values (4)80000
16.0%

age
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.95706
Minimum18
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:24.889814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q126
median35
Q343
95-th percentile54
Maximum70
Range52
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.118603
Coefficient of variation (CV)0.31806459
Kurtosis-0.43633607
Mean34.95706
Median Absolute Deviation (MAD)8
Skewness0.33586008
Sum1747853
Variance123.62333
MonotonicityNot monotonic
2025-11-25T13:47:25.040932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184562
 
9.1%
351713
 
3.4%
341695
 
3.4%
371668
 
3.3%
361662
 
3.3%
321628
 
3.3%
331620
 
3.2%
301571
 
3.1%
311561
 
3.1%
381534
 
3.1%
Other values (43)30786
61.6%
ValueCountFrequency (%)
184562
9.1%
19715
 
1.4%
20841
 
1.7%
21875
 
1.8%
22927
 
1.9%
231069
 
2.1%
241138
 
2.3%
251279
 
2.6%
261270
 
2.5%
271370
 
2.7%
ValueCountFrequency (%)
7088
0.2%
6924
 
< 0.1%
6829
 
0.1%
6741
 
0.1%
6658
 
0.1%
6562
0.1%
6485
0.2%
6394
0.2%
6299
0.2%
61150
0.3%

occupation_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Employed
34971 
Self-Employed
10179 
Student
4850 

Length

Max length13
Median length8
Mean length8.9209
Min length7

Characters and Unicode

Total characters446045
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmployed
2nd rowEmployed
3rd rowStudent
4th rowStudent
5th rowEmployed

Common Values

ValueCountFrequency (%)
Employed34971
69.9%
Self-Employed10179
 
20.4%
Student4850
 
9.7%

Length

2025-11-25T13:47:25.174633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T13:47:25.269388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
employed34971
69.9%
self-employed10179
 
20.4%
student4850
 
9.7%

Most occurring characters

ValueCountFrequency (%)
e60179
13.5%
l55329
12.4%
d50000
11.2%
E45150
10.1%
m45150
10.1%
p45150
10.1%
o45150
10.1%
y45150
10.1%
S15029
 
3.4%
f10179
 
2.3%
Other values (4)29579
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)446045
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e60179
13.5%
l55329
12.4%
d50000
11.2%
E45150
10.1%
m45150
10.1%
p45150
10.1%
o45150
10.1%
y45150
10.1%
S15029
 
3.4%
f10179
 
2.3%
Other values (4)29579
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)446045
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e60179
13.5%
l55329
12.4%
d50000
11.2%
E45150
10.1%
m45150
10.1%
p45150
10.1%
o45150
10.1%
y45150
10.1%
S15029
 
3.4%
f10179
 
2.3%
Other values (4)29579
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)446045
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e60179
13.5%
l55329
12.4%
d50000
11.2%
E45150
10.1%
m45150
10.1%
p45150
10.1%
o45150
10.1%
y45150
10.1%
S15029
 
3.4%
f10179
 
2.3%
Other values (4)29579
6.6%

years_employed
Real number (ℝ)

High correlation  Zeros 

Distinct395
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.454868
Minimum0
Maximum39.9
Zeros3459
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:25.407465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.3
median4.9
Q311.4
95-th percentile23.2
Maximum39.9
Range39.9
Interquartile range (IQR)10.1

Descriptive statistics

Standard deviation7.6120967
Coefficient of variation (CV)1.0210907
Kurtosis1.262256
Mean7.454868
Median Absolute Deviation (MAD)4
Skewness1.2936126
Sum372743.4
Variance57.944017
MonotonicityNot monotonic
2025-11-25T13:47:25.577429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03459
 
6.9%
12384
 
4.8%
1.7641
 
1.3%
1.2636
 
1.3%
1.5617
 
1.2%
1.3603
 
1.2%
1.4598
 
1.2%
1.6594
 
1.2%
0.7587
 
1.2%
0.4587
 
1.2%
Other values (385)39294
78.6%
ValueCountFrequency (%)
03459
6.9%
0.1579
 
1.2%
0.2516
 
1.0%
0.3537
 
1.1%
0.4587
 
1.2%
0.5566
 
1.1%
0.6528
 
1.1%
0.7587
 
1.2%
0.8533
 
1.1%
0.9536
 
1.1%
ValueCountFrequency (%)
39.94
< 0.1%
39.73
< 0.1%
39.62
 
< 0.1%
39.57
< 0.1%
39.44
< 0.1%
39.21
 
< 0.1%
39.11
 
< 0.1%
396
< 0.1%
38.93
< 0.1%
38.82
 
< 0.1%

annual_income
Real number (ℝ)

High correlation 

Distinct35770
Distinct (%)71.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50062.892
Minimum15000
Maximum250000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:25.753630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15000
5-th percentile15000
Q127280.5
median41607.5
Q362723.25
95-th percentile113904.4
Maximum250000
Range235000
Interquartile range (IQR)35442.75

Descriptive statistics

Standard deviation32630.501
Coefficient of variation (CV)0.65179017
Kurtosis5.227919
Mean50062.892
Median Absolute Deviation (MAD)16529.5
Skewness1.8878689
Sum2.5031446 × 109
Variance1.0647496 × 109
MonotonicityNot monotonic
2025-11-25T13:47:25.912774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150002644
 
5.3%
25000059
 
0.1%
240607
 
< 0.1%
275787
 
< 0.1%
219727
 
< 0.1%
213076
 
< 0.1%
231046
 
< 0.1%
355186
 
< 0.1%
403126
 
< 0.1%
439316
 
< 0.1%
Other values (35760)47246
94.5%
ValueCountFrequency (%)
150002644
5.3%
150011
 
< 0.1%
150051
 
< 0.1%
150072
 
< 0.1%
150082
 
< 0.1%
150112
 
< 0.1%
150141
 
< 0.1%
150191
 
< 0.1%
150242
 
< 0.1%
150261
 
< 0.1%
ValueCountFrequency (%)
25000059
0.1%
2490711
 
< 0.1%
2488461
 
< 0.1%
2482501
 
< 0.1%
2478351
 
< 0.1%
2476211
 
< 0.1%
2465981
 
< 0.1%
2454241
 
< 0.1%
2453821
 
< 0.1%
2442861
 
< 0.1%

credit_score
Real number (ℝ)

High correlation 

Distinct432
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean643.61482
Minimum348
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:26.065605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum348
5-th percentile538
Q1600
median643
Q3687
95-th percentile751
Maximum850
Range502
Interquartile range (IQR)87

Descriptive statistics

Standard deviation64.731518
Coefficient of variation (CV)0.10057493
Kurtosis-0.043807213
Mean643.61482
Median Absolute Deviation (MAD)44
Skewness0.012996168
Sum32180741
Variance4190.1695
MonotonicityNot monotonic
2025-11-25T13:47:26.214047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
646334
 
0.7%
641332
 
0.7%
628327
 
0.7%
651320
 
0.6%
648320
 
0.6%
625319
 
0.6%
663318
 
0.6%
635318
 
0.6%
657316
 
0.6%
650314
 
0.6%
Other values (422)46782
93.6%
ValueCountFrequency (%)
3481
 
< 0.1%
3751
 
< 0.1%
3961
 
< 0.1%
4011
 
< 0.1%
4021
 
< 0.1%
4081
 
< 0.1%
4101
 
< 0.1%
4141
 
< 0.1%
4213
< 0.1%
4221
 
< 0.1%
ValueCountFrequency (%)
85043
0.1%
8491
 
< 0.1%
8481
 
< 0.1%
8471
 
< 0.1%
8461
 
< 0.1%
8441
 
< 0.1%
8438
 
< 0.1%
8421
 
< 0.1%
8411
 
< 0.1%
8403
 
< 0.1%

credit_history_years
Real number (ℝ)

High correlation 

Distinct301
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.168274
Minimum0
Maximum30
Zeros302
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:26.357852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q12
median6.1
Q312.6
95-th percentile23
Maximum30
Range30
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation7.2075523
Coefficient of variation (CV)0.88238376
Kurtosis0.075125989
Mean8.168274
Median Absolute Deviation (MAD)4.6
Skewness0.95375516
Sum408413.7
Variance51.94881
MonotonicityNot monotonic
2025-11-25T13:47:26.502193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6677
 
1.4%
0.6667
 
1.3%
0.5665
 
1.3%
0.7660
 
1.3%
1.5656
 
1.3%
0.8654
 
1.3%
1.9653
 
1.3%
1.3650
 
1.3%
1.7645
 
1.3%
1.1640
 
1.3%
Other values (291)43433
86.9%
ValueCountFrequency (%)
0302
0.6%
0.1623
1.2%
0.2615
1.2%
0.3630
1.3%
0.4631
1.3%
0.5665
1.3%
0.6667
1.3%
0.7660
1.3%
0.8654
1.3%
0.9618
1.2%
ValueCountFrequency (%)
3011
 
< 0.1%
29.923
< 0.1%
29.819
< 0.1%
29.719
< 0.1%
29.627
0.1%
29.529
0.1%
29.420
< 0.1%
29.326
0.1%
29.217
< 0.1%
29.120
< 0.1%

savings_assets
Real number (ℝ)

High correlation 

Distinct10382
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3595.6194
Minimum0
Maximum300000
Zeros435
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:26.650394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q1130
median568
Q32271
95-th percentile14963.15
Maximum300000
Range300000
Interquartile range (IQR)2141

Descriptive statistics

Standard deviation13232.399
Coefficient of variation (CV)3.6801446
Kurtosis203.38951
Mean3595.6194
Median Absolute Deviation (MAD)527
Skewness12.054946
Sum1.7978097 × 108
Variance1.7509639 × 108
MonotonicityNot monotonic
2025-11-25T13:47:26.800692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0435
 
0.9%
7201
 
0.4%
3200
 
0.4%
9190
 
0.4%
5187
 
0.4%
4187
 
0.4%
6178
 
0.4%
8176
 
0.4%
1175
 
0.4%
2172
 
0.3%
Other values (10372)47899
95.8%
ValueCountFrequency (%)
0435
0.9%
1175
0.4%
2172
 
0.3%
3200
0.4%
4187
0.4%
5187
0.4%
6178
0.4%
7201
0.4%
8176
0.4%
9190
0.4%
ValueCountFrequency (%)
30000019
< 0.1%
2909141
 
< 0.1%
2891821
 
< 0.1%
2830661
 
< 0.1%
2771321
 
< 0.1%
2738491
 
< 0.1%
2734521
 
< 0.1%
2709801
 
< 0.1%
2688801
 
< 0.1%
2684481
 
< 0.1%

current_debt
Real number (ℝ)

High correlation 

Distinct25350
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14290.442
Minimum60
Maximum163344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:26.953434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile2018.95
Q15581
median10385
Q318449.25
95-th percentile39815.4
Maximum163344
Range163284
Interquartile range (IQR)12868.25

Descriptive statistics

Standard deviation13243.757
Coefficient of variation (CV)0.92675631
Kurtosis9.2589019
Mean14290.442
Median Absolute Deviation (MAD)5732
Skewness2.4378369
Sum7.1452211 × 108
Variance1.7539711 × 108
MonotonicityNot monotonic
2025-11-25T13:47:27.101503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
570612
 
< 0.1%
437711
 
< 0.1%
578710
 
< 0.1%
362410
 
< 0.1%
361510
 
< 0.1%
53719
 
< 0.1%
27109
 
< 0.1%
39209
 
< 0.1%
44389
 
< 0.1%
90169
 
< 0.1%
Other values (25340)49902
99.8%
ValueCountFrequency (%)
601
< 0.1%
642
< 0.1%
761
< 0.1%
851
< 0.1%
951
< 0.1%
1031
< 0.1%
1171
< 0.1%
1181
< 0.1%
1201
< 0.1%
1221
< 0.1%
ValueCountFrequency (%)
1633441
< 0.1%
1452791
< 0.1%
1417381
< 0.1%
1361261
< 0.1%
1358551
< 0.1%
1350081
< 0.1%
1320631
< 0.1%
1319181
< 0.1%
1250501
< 0.1%
1243221
< 0.1%

defaults_on_file
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
0
47326 
1
 
2674

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
047326
94.7%
12674
 
5.3%

Length

2025-11-25T13:47:27.264673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T13:47:27.350252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
047326
94.7%
12674
 
5.3%

Most occurring characters

ValueCountFrequency (%)
047326
94.7%
12674
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
047326
94.7%
12674
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
047326
94.7%
12674
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
047326
94.7%
12674
 
5.3%

delinquencies_last_2yrs
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55464
Minimum0
Maximum9
Zeros30797
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:27.419588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84504956
Coefficient of variation (CV)1.5236001
Kurtosis4.0913995
Mean0.55464
Median Absolute Deviation (MAD)0
Skewness1.8169443
Sum27732
Variance0.71410875
MonotonicityNot monotonic
2025-11-25T13:47:27.529814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
030797
61.6%
113043
26.1%
24399
 
8.8%
31280
 
2.6%
4384
 
0.8%
577
 
0.2%
613
 
< 0.1%
75
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
030797
61.6%
113043
26.1%
24399
 
8.8%
31280
 
2.6%
4384
 
0.8%
577
 
0.2%
613
 
< 0.1%
75
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
81
 
< 0.1%
75
 
< 0.1%
613
 
< 0.1%
577
 
0.2%
4384
 
0.8%
31280
 
2.6%
24399
 
8.8%
113043
26.1%
030797
61.6%

derogatory_marks
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
0
43607 
1
5524 
2
 
758
3
 
102
4
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
043607
87.2%
15524
 
11.0%
2758
 
1.5%
3102
 
0.2%
49
 
< 0.1%

Length

2025-11-25T13:47:27.679424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T13:47:27.797666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
043607
87.2%
15524
 
11.0%
2758
 
1.5%
3102
 
0.2%
49
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
043607
87.2%
15524
 
11.0%
2758
 
1.5%
3102
 
0.2%
49
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
043607
87.2%
15524
 
11.0%
2758
 
1.5%
3102
 
0.2%
49
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
043607
87.2%
15524
 
11.0%
2758
 
1.5%
3102
 
0.2%
49
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
043607
87.2%
15524
 
11.0%
2758
 
1.5%
3102
 
0.2%
49
 
< 0.1%

product_type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Credit Card
22455 
Personal Loan
17523 
Line of Credit
10022 

Length

Max length14
Median length13
Mean length12.30224
Min length11

Characters and Unicode

Total characters615112
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowPersonal Loan
3rd rowCredit Card
4th rowCredit Card
5th rowPersonal Loan

Common Values

ValueCountFrequency (%)
Credit Card22455
44.9%
Personal Loan17523
35.0%
Line of Credit10022
20.0%

Length

2025-11-25T13:47:27.936329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T13:47:28.030694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
credit32477
29.5%
card22455
20.4%
personal17523
15.9%
loan17523
15.9%
line10022
 
9.1%
of10022
 
9.1%

Most occurring characters

ValueCountFrequency (%)
r72455
11.8%
e60022
9.8%
60022
9.8%
a57501
9.3%
C54932
8.9%
d54932
8.9%
o45068
7.3%
n45068
7.3%
i42499
6.9%
t32477
 
5.3%
Other values (5)90136
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)615112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r72455
11.8%
e60022
9.8%
60022
9.8%
a57501
9.3%
C54932
8.9%
d54932
8.9%
o45068
7.3%
n45068
7.3%
i42499
6.9%
t32477
 
5.3%
Other values (5)90136
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)615112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r72455
11.8%
e60022
9.8%
60022
9.8%
a57501
9.3%
C54932
8.9%
d54932
8.9%
o45068
7.3%
n45068
7.3%
i42499
6.9%
t32477
 
5.3%
Other values (5)90136
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)615112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r72455
11.8%
e60022
9.8%
60022
9.8%
a57501
9.3%
C54932
8.9%
d54932
8.9%
o45068
7.3%
n45068
7.3%
i42499
6.9%
t32477
 
5.3%
Other values (5)90136
14.7%

loan_intent
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Personal
12429 
Education
10134 
Medical
7598 
Business
7469 
Home Improvement
7453 

Length

Max length18
Median length16
Mean length10.2266
Min length7

Characters and Unicode

Total characters511330
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBusiness
2nd rowHome Improvement
3rd rowDebt Consolidation
4th rowBusiness
5th rowEducation

Common Values

ValueCountFrequency (%)
Personal12429
24.9%
Education10134
20.3%
Medical7598
15.2%
Business7469
14.9%
Home Improvement7453
14.9%
Debt Consolidation4917
 
9.8%

Length

2025-11-25T13:47:28.161557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T13:47:28.273793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
personal12429
19.9%
education10134
16.2%
medical7598
12.2%
business7469
12.0%
home7453
11.9%
improvement7453
11.9%
debt4917
 
7.9%
consolidation4917
 
7.9%

Most occurring characters

ValueCountFrequency (%)
e54772
 
10.7%
o52220
 
10.2%
n47319
 
9.3%
s39753
 
7.8%
a35078
 
6.9%
i35035
 
6.9%
t27421
 
5.4%
l24944
 
4.9%
d22649
 
4.4%
m22359
 
4.4%
Other values (15)149780
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)511330
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e54772
 
10.7%
o52220
 
10.2%
n47319
 
9.3%
s39753
 
7.8%
a35078
 
6.9%
i35035
 
6.9%
t27421
 
5.4%
l24944
 
4.9%
d22649
 
4.4%
m22359
 
4.4%
Other values (15)149780
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)511330
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e54772
 
10.7%
o52220
 
10.2%
n47319
 
9.3%
s39753
 
7.8%
a35078
 
6.9%
i35035
 
6.9%
t27421
 
5.4%
l24944
 
4.9%
d22649
 
4.4%
m22359
 
4.4%
Other values (15)149780
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)511330
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e54772
 
10.7%
o52220
 
10.2%
n47319
 
9.3%
s39753
 
7.8%
a35078
 
6.9%
i35035
 
6.9%
t27421
 
5.4%
l24944
 
4.9%
d22649
 
4.4%
m22359
 
4.4%
Other values (15)149780
29.3%

loan_amount
Real number (ℝ)

High correlation 

Distinct996
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33041.874
Minimum500
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:28.458280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q112300
median26100
Q348500
95-th percentile90900
Maximum100000
Range99500
Interquartile range (IQR)36200

Descriptive statistics

Standard deviation26116.185
Coefficient of variation (CV)0.79039661
Kurtosis0.085771957
Mean33041.874
Median Absolute Deviation (MAD)16300
Skewness0.93149232
Sum1.6520937 × 109
Variance6.8205512 × 108
MonotonicityNot monotonic
2025-11-25T13:47:28.624817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700002554
 
5.1%
1000002014
 
4.0%
1500597
 
1.2%
50000553
 
1.1%
5000419
 
0.8%
1800178
 
0.4%
1600150
 
0.3%
2300148
 
0.3%
1900144
 
0.3%
2600138
 
0.3%
Other values (986)43105
86.2%
ValueCountFrequency (%)
500116
0.2%
60041
 
0.1%
70039
 
0.1%
80042
 
0.1%
90044
 
0.1%
1000132
0.3%
110081
0.2%
120066
0.1%
130065
0.1%
140064
0.1%
ValueCountFrequency (%)
1000002014
4.0%
999003
 
< 0.1%
998001
 
< 0.1%
997004
 
< 0.1%
996005
 
< 0.1%
995009
 
< 0.1%
994008
 
< 0.1%
993004
 
< 0.1%
992002
 
< 0.1%
991004
 
< 0.1%

interest_rate
Real number (ℝ)

High correlation 

Distinct1687
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.498591
Minimum6
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:28.774427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9.19
Q112.18
median15.44
Q318.87
95-th percentile22.12
Maximum23
Range17
Interquartile range (IQR)6.69

Descriptive statistics

Standard deviation4.067942
Coefficient of variation (CV)0.26247173
Kurtosis-0.98897991
Mean15.498591
Median Absolute Deviation (MAD)3.34
Skewness0.019608004
Sum774929.54
Variance16.548152
MonotonicityNot monotonic
2025-11-25T13:47:28.928601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.4768
 
0.1%
11.0767
 
0.1%
11.4361
 
0.1%
11.4861
 
0.1%
11.257
 
0.1%
11.0657
 
0.1%
11.1857
 
0.1%
11.2357
 
0.1%
16.5757
 
0.1%
11.2956
 
0.1%
Other values (1677)49402
98.8%
ValueCountFrequency (%)
61
 
< 0.1%
6.024
< 0.1%
6.031
 
< 0.1%
6.043
< 0.1%
6.051
 
< 0.1%
6.067
< 0.1%
6.073
< 0.1%
6.084
< 0.1%
6.13
< 0.1%
6.113
< 0.1%
ValueCountFrequency (%)
2315
< 0.1%
22.9933
0.1%
22.9821
< 0.1%
22.9722
< 0.1%
22.9632
0.1%
22.9536
0.1%
22.9415
< 0.1%
22.9320
< 0.1%
22.9233
0.1%
22.9130
0.1%

debt_to_income_ratio
Real number (ℝ)

High correlation 

Distinct796
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28572416
Minimum0.002
Maximum0.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:29.095851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.063
Q10.161
median0.265
Q30.389
95-th percentile0.585
Maximum0.8
Range0.798
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation0.15978652
Coefficient of variation (CV)0.5592335
Kurtosis-0.15115295
Mean0.28572416
Median Absolute Deviation (MAD)0.113
Skewness0.5914932
Sum14286.208
Variance0.025531733
MonotonicityNot monotonic
2025-11-25T13:47:29.250816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.221155
 
0.3%
0.197148
 
0.3%
0.224147
 
0.3%
0.151143
 
0.3%
0.169143
 
0.3%
0.195143
 
0.3%
0.269140
 
0.3%
0.203138
 
0.3%
0.236135
 
0.3%
0.25134
 
0.3%
Other values (786)48574
97.1%
ValueCountFrequency (%)
0.0024
 
< 0.1%
0.0034
 
< 0.1%
0.0047
 
< 0.1%
0.0058
 
< 0.1%
0.00613
< 0.1%
0.00714
< 0.1%
0.0088
 
< 0.1%
0.00914
< 0.1%
0.0117
< 0.1%
0.01121
< 0.1%
ValueCountFrequency (%)
0.878
0.2%
0.7994
 
< 0.1%
0.7983
 
< 0.1%
0.7972
 
< 0.1%
0.7961
 
< 0.1%
0.7952
 
< 0.1%
0.7943
 
< 0.1%
0.7926
 
< 0.1%
0.7915
 
< 0.1%
0.792
 
< 0.1%

loan_to_income_ratio
Real number (ℝ)

High correlation 

Distinct1992
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70199866
Minimum0.008
Maximum2.001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:29.424668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.099
Q10.333
median0.622
Q31.01025
95-th percentile1.624
Maximum2.001
Range1.993
Interquartile range (IQR)0.67725

Descriptive statistics

Standard deviation0.46578752
Coefficient of variation (CV)0.66351625
Kurtosis-0.27296493
Mean0.70199866
Median Absolute Deviation (MAD)0.326
Skewness0.65752654
Sum35099.933
Variance0.21695802
MonotonicityNot monotonic
2025-11-25T13:47:29.588141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1991
 
2.0%
0.101403
 
0.8%
0.099402
 
0.8%
0.098191
 
0.4%
0.102186
 
0.4%
0.333172
 
0.3%
0.38770
 
0.1%
0.55366
 
0.1%
0.44765
 
0.1%
0.4663
 
0.1%
Other values (1982)47391
94.8%
ValueCountFrequency (%)
0.0081
 
< 0.1%
0.015
 
< 0.1%
0.0117
 
< 0.1%
0.01215
< 0.1%
0.01317
< 0.1%
0.0149
< 0.1%
0.01515
< 0.1%
0.01613
< 0.1%
0.01722
< 0.1%
0.01821
< 0.1%
ValueCountFrequency (%)
2.0014
< 0.1%
26
< 0.1%
1.9994
< 0.1%
1.9986
< 0.1%
1.9972
 
< 0.1%
1.9962
 
< 0.1%
1.9953
< 0.1%
1.9944
< 0.1%
1.9934
< 0.1%
1.9927
< 0.1%

payment_to_income_ratio
Real number (ℝ)

High correlation 

Distinct665
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23399494
Minimum0.003
Maximum0.667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-11-25T13:47:29.749624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.033
Q10.111
median0.207
Q30.337
95-th percentile0.541
Maximum0.667
Range0.664
Interquartile range (IQR)0.226

Descriptive statistics

Standard deviation0.1552681
Coefficient of variation (CV)0.66355322
Kurtosis-0.27310773
Mean0.23399494
Median Absolute Deviation (MAD)0.109
Skewness0.65738493
Sum11699.747
Variance0.024108182
MonotonicityNot monotonic
2025-11-25T13:47:29.904974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0331583
 
3.2%
0.034638
 
1.3%
0.111251
 
0.5%
0.129167
 
0.3%
0.176156
 
0.3%
0.189155
 
0.3%
0.156152
 
0.3%
0.151151
 
0.3%
0.118149
 
0.3%
0.178148
 
0.3%
Other values (655)46450
92.9%
ValueCountFrequency (%)
0.0036
 
< 0.1%
0.00439
 
0.1%
0.00537
 
0.1%
0.00657
0.1%
0.00765
0.1%
0.00858
0.1%
0.00970
0.1%
0.0171
0.1%
0.011100
0.2%
0.01293
0.2%
ValueCountFrequency (%)
0.66710
< 0.1%
0.66612
< 0.1%
0.6659
< 0.1%
0.66414
< 0.1%
0.6637
 
< 0.1%
0.66220
< 0.1%
0.6619
< 0.1%
0.6622
< 0.1%
0.65914
< 0.1%
0.65814
< 0.1%

loan_status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
1
27523 
0
22477 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
127523
55.0%
022477
45.0%

Length

2025-11-25T13:47:30.068714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T13:47:30.167572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
127523
55.0%
022477
45.0%

Most occurring characters

ValueCountFrequency (%)
127523
55.0%
022477
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
127523
55.0%
022477
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
127523
55.0%
022477
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
127523
55.0%
022477
45.0%

Interactions

2025-11-25T13:47:21.585068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:00.000408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:01.564412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:03.565139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:05.300540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:06.977324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:08.698147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:10.709896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:12.590302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:14.219489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:15.846178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:17.518127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:19.377193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:21.707743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:00.116416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:01.683201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:03.683738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:05.449290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:07.099949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:08.814928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:10.852608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:12.703430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:14.345707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:15.963320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:17.645029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:19.508035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:21.839691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:00.234570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:01.810827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:03.820304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:05.583848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:07.230866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:08.948192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:10.994512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:12.823712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:14.480985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:16.090327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:17.783706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:19.650455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:21.965894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:00.346733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:01.937179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:03.938287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:05.703895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:07.370902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:09.066240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:11.130388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:12.943199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:14.596428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:16.210703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:17.910416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:20.168557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:22.086881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:00.457765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:02.056304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:04.057420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:05.813919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:07.497216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:09.190252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:11.258220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:13.052576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:14.717396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:16.327454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:18.033548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:20.291482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:22.210694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:00.575072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:02.179621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:04.224774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:05.936653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:07.656503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:09.319889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:11.392203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:13.204161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:14.841239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:16.452500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:18.165983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:20.437523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:22.349525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:00.700444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:02.594699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:04.348058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:06.068650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:07.787415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:09.443249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:11.536997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:13.343731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:14.970474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:16.600485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:18.304204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:20.575406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:22.490694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:00.832616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:02.736783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:04.486439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:06.203490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:07.929791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:09.579921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:11.735568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:13.495893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:15.101367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:16.740920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:18.447963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:20.729242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:22.617523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:00.951503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:02.856150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:04.606826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:06.334695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:08.046765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:09.710404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:11.870557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:13.606797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:15.215267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:16.868291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:18.573725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:20.856715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:22.748057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:01.066286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:02.988235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:04.726837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:06.459678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:08.168517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:09.840432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:12.001880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:13.727849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:15.327325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:16.994086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:18.715882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:20.990408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:22.885196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:01.184978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:03.134947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:04.863826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:06.581394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:08.292788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:09.976139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:12.152268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:13.844829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:15.457310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:17.112014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:18.917696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:21.158181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:23.028377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:01.312553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:03.282285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:05.015865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:06.713518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:08.429588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:10.434109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:12.297405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:13.968300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:15.584203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:17.250578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:19.057304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:21.304971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:23.168793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:01.440513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:03.427277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:05.152076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:06.845458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:08.566908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:10.570281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:12.446351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:14.093328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:15.718775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:17.389424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:19.216057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T13:47:21.444377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-25T13:47:30.266809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageannual_incomecredit_history_yearscredit_scorecurrent_debtdebt_to_income_ratiodefaults_on_filedelinquencies_last_2yrsderogatory_marksinterest_rateloan_amountloan_intentloan_statusloan_to_income_ratiooccupation_statuspayment_to_income_ratioproduct_typesavings_assetsyears_employed
age1.0000.3100.6640.3590.199-0.0080.063-0.1060.050-0.1790.1690.0010.317-0.0320.000-0.0320.0000.4580.643
annual_income0.3101.0000.2050.1860.657-0.0020.035-0.0550.020-0.0950.5810.0050.157-0.0390.264-0.0390.0000.3900.337
credit_history_years0.6640.2051.0000.2360.135-0.0020.046-0.0660.033-0.1190.1100.0000.297-0.0220.000-0.0220.0100.6270.458
credit_score0.3590.1860.2361.0000.120-0.0050.259-0.3180.184-0.4870.1050.0000.540-0.0170.022-0.0170.0000.1900.234
current_debt0.1990.6570.1350.1201.0000.7160.018-0.0340.013-0.0580.3810.0050.085-0.0340.169-0.0340.0000.2620.218
debt_to_income_ratio-0.008-0.002-0.002-0.0050.7161.0000.0130.0040.0080.007-0.0010.0000.3690.0000.0130.0000.0030.004-0.006
defaults_on_file0.0630.0350.0460.2590.0180.0131.0000.2580.0990.1810.0150.0000.2630.0030.0000.0030.0000.0080.040
delinquencies_last_2yrs-0.106-0.055-0.066-0.318-0.0340.0040.2581.0000.0420.165-0.0330.0000.3210.0020.0000.0020.000-0.059-0.066
derogatory_marks0.0500.0200.0330.1840.0130.0080.0990.0421.0000.1280.0100.0000.2270.0060.0100.0060.0000.0000.031
interest_rate-0.179-0.095-0.119-0.487-0.0580.0070.1810.1650.1281.000-0.1050.0000.376-0.0640.016-0.0640.670-0.095-0.120
loan_amount0.1690.5810.1100.1050.381-0.0010.015-0.0330.010-0.1051.0000.0000.0840.7550.2660.7550.3590.2260.235
loan_intent0.0010.0050.0000.0000.0050.0000.0000.0000.0000.0000.0001.0000.1920.0000.0000.0000.0000.0060.000
loan_status0.3170.1570.2970.5400.0850.3690.2630.3210.2270.3760.0840.1921.0000.1970.0210.1970.1200.0690.222
loan_to_income_ratio-0.032-0.039-0.022-0.017-0.0340.0000.0030.0020.006-0.0640.7550.0000.1971.0000.2361.0000.410-0.0230.048
occupation_status0.0000.2640.0000.0220.1690.0130.0000.0000.0100.0160.2660.0000.0210.2361.0000.2360.0040.0290.263
payment_to_income_ratio-0.032-0.039-0.022-0.017-0.0340.0000.0030.0020.006-0.0640.7550.0000.1971.0000.2361.0000.411-0.0230.048
product_type0.0000.0000.0100.0000.0000.0030.0000.0000.0000.6700.3590.0000.1200.4100.0040.4111.0000.0000.012
savings_assets0.4580.3900.6270.1900.2620.0040.008-0.0590.000-0.0950.2260.0060.069-0.0230.029-0.0230.0001.0000.358
years_employed0.6430.3370.4580.2340.218-0.0060.040-0.0660.031-0.1200.2350.0000.2220.0480.2630.0480.0120.3581.000

Missing values

2025-11-25T13:47:23.407456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T13:47:23.725294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idageoccupation_statusyears_employedannual_incomecredit_scorecredit_history_yearssavings_assetscurrent_debtdefaults_on_filedelinquencies_last_2yrsderogatory_marksproduct_typeloan_intentloan_amountinterest_ratedebt_to_income_ratioloan_to_income_ratiopayment_to_income_ratioloan_status
0CUST10000040Employed17.2255796925.389510820000Credit CardBusiness60017.020.4230.0230.0081
1CUST10000133Employed7.3430876273.516916550010Personal LoanHome Improvement5330014.100.3841.2370.4120
2CUST10000242Student1.1208406898.4177852000Credit CardDebt Consolidation210018.330.3770.1010.0341
3CUST10000353Student0.5291476929.8148011603010Credit CardBusiness290018.740.3980.0990.0331
4CUST10000432Employed12.5636576307.220912424000Personal LoanEducation9960013.920.1951.5650.5221
5CUST10000532Employed13.4320155707.32531120002Credit CardPersonal3700022.920.0351.1560.3850
6CUST10000653Employed22.94498967411.11966719298000Personal LoanHome Improvement4560011.020.4291.0140.3381
7CUST10000744Self-Employed4.28060362518.583038382000Credit CardPersonal5170019.420.4760.6410.2141
8CUST10000829Employed5.9284165692.6133422668120Credit CardEducation3380022.720.7981.1890.3960
9CUST10000941Employed7.07071763821.5157821394010Credit CardPersonal7000019.350.3030.9900.3301
customer_idageoccupation_statusyears_employedannual_incomecredit_scorecredit_history_yearssavings_assetscurrent_debtdefaults_on_filedelinquencies_last_2yrsderogatory_marksproduct_typeloan_intentloan_amountinterest_ratedebt_to_income_ratioloan_to_income_ratiopayment_to_income_ratioloan_status
49990CUST14999037Employed9.43721858811.7238913416000Credit CardPersonal1060021.350.3600.2850.0950
49991CUST14999126Employed0.7221595503.7117412699000Credit CardPersonal2460021.520.5731.1100.3700
49992CUST14999230Employed9.3247976108.3166282010Personal LoanMedical3210014.980.2531.2950.4320
49993CUST14999353Employed18.1369486762.65911421001Credit CardPersonal240018.060.3090.0650.0221
49994CUST14999418Employed0.0643435851.327727914002Personal LoanPersonal4160015.420.4340.6470.2160
49995CUST14999535Employed4.33944957016.311277576000Credit CardEducation4280021.310.1921.0850.3620
49996CUST14999634Employed4.42049667212.614786276100Credit CardPersonal380018.070.3060.1850.0620
49997CUST14999741Self-Employed4.81874371910.11710331000Credit CardPersonal1800017.450.5510.9600.3200
49998CUST14999838Student0.4172506331.357779001Personal LoanPersonal140014.710.4510.0810.0270
49999CUST14999953Employed17.24692369515.083322655010Personal LoanPersonal4100012.390.0570.8740.2911